Download What is Artificial Intelligence?

Introduction to Artificial
Intelligence
Kalev Kask
ICS 271
Fall 2014
http://www.ics.uci.edu/~kkask/Fall-2014 CS271/
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Course requirements
Assignments:
• There will be weekly homework assignments, a project, a final.
Course-Grade:
• Homework will account for 20% of the grade, project 30%, final 50% of the
grade.
. Discussion:
• Optional. TBD.
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Course overview
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Introduction and Agents (chapters 1,2)
Search (chapters 3,4,5,6)
Logic (chapters 7,8,9)
Planning (chapters 10,11)
Uncertainty and probability (chapters 13-14)
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Plan of the course
Part I Artificial Intelligence
1 Introduction
2 Intelligent Agents
Part II Problem Solving
3 Solving Problems by Searching
4 Beyond Classical Search
5 Adversarial Search
6 Constraint Satisfaction Problems
Part III Knowledge and Reasoning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Classical Planning
11 Planning and Acting in the Real World
Part IV Uncertainty
13 Uncertainly
14 Probabilistic Reasoning
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Resources on the internet
Resources on the Internet
• AI on the Web: A very comprehensive list of Web resources
about AI from the Russell and Norvig textbook.
Essays and Papers
• What is AI, John McCarthy
• Computing Machinery and Intelligence, A.M. Turing
• Rethinking Artificial Intelligence, Patrick H.Winston
• AI Topics: http://aitopics.net/index.php
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Today’s class
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What is Artificial Intelligence?
A brief History
Intelligent agents
State of the art
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Today’s class
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What is Artificial Intelligence?
A brief History
Intelligent agents
State of the art
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What is Artificial Intelligence
(John McCarthy , Basic Questions)
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What is artificial intelligence?
It is the science and engineering of making intelligent machines, especially intelligent
computer programs. It is related to the similar task of using computers to
understand human intelligence, but AI does not have to confine itself to methods
that are biologically observable.
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Yes, but what is intelligence?
Intelligence is the computational part of the ability to achieve goals in the world.
Varying kinds and degrees of intelligence occur in people, many animals and some
machines.
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Isn't there a solid definition of intelligence that doesn't depend on relating it to
human intelligence?
Not yet. The problem is that we cannot yet characterize in general what kinds of
computational procedures we want to call intelligent. We understand some of the
mechanisms of intelligence and not others.
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More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
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What is Artificial Intelligence?
• Human-like vs rational-like
• Thought processes vs behavior
• How to simulate humans intellect and behavior by a
machine.
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Mathematical problems (puzzles, games, theorems)
Common-sense reasoning
Expert knowledge: lawyers, medicine, diagnosis
Social behavior
• Things we would call “intelligent” if done by a
human.
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What is AI?
Views of AI fall into four categories:
Thinking humanly
Acting humanly
Thinking rationally
Acting rationally
The textbook advocates "acting rationally“
How to simulate humans intellect and behavior by a machine.
Mathematical problems (puzzles, games, theorems)
Common-sense reasoning
Expert knowledge: lawyers, medicine, diagnosis
Social behavior
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The Turing Test
(Can Machine think? A. M. Turing, 1950)
http://aitopics.net/index.php
http://amturing.acm.org/acm_tcc_webcasts.cfm
• Requires:
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Natural language
Knowledge representation
Automated reasoning
Machine learning
(vision, robotics) for full test
Acting/Thinking Humanly/Rationally
• Turing test (1950)
• Requires:
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Natural language
Knowledge representation
automated reasoning
machine learning
(vision, robotics.) for full test
• Methods for Thinking Humanly:
– Introspection, the general problem solver (Newell and
Simon 1961)
– Cognitive sciences
• Thinking rationally:
– Logic
– Problems: how to represent and reason in a domain
• Acting rationally:
– Agents: Perceive and act
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What is Artificial Intelligence
• Thought processes
– “The exciting new effort to make computers think ..
Machines with minds, in the full and literal sense”
(Haugeland, 1985)
• Behavior
– “The study of how to make computers do things at which, at
the moment, people are better.” (Rich, and Knight, 1991)
• Activities
– The automation of activities that we associate with human
thinking, activities such as decision-making, problem solving,
learning… (Bellman)
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The foundation of AI
Philosophy, Mathematics, Economics, Neuroscience, Psychology,
Computer Engineering,
Features of intelligent system
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Deduction, reasoning, problem solving
Knowledge representation
Planning
Learning
Natural language processing
Perception
Motion and manipulation
Tools
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Search and optimization
Logic
Probabilistic reasoning
Neural networks
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Today’s class
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What is Artificial Intelligence?
A brief history
Intelligent agents
State of the art
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Histroy of AI
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McCulloch and Pitts (1943)
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Minsky and Edmonds (1951)
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Built a neural net computer
Darmouth conference (1956):
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Neural networks that learn
McCarthy, Minsky, Newell, Simon met,
Logic theorist (LT)- Of Newell and Simon proves a theorem in Principia
Mathematica-Russel.
The name “Artficial Intelligence” was coined.
1952-1969 (early enthusiasm, great expectations)
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GPS- Newell and Simon
Geometry theorem prover - Gelernter (1959)
Samuel Checkers that learns (1952)
McCarthy - Lisp (1958), Advice Taker, Robinson’s resolution
Microworlds: Integration, block-worlds.
1962- the perceptron convergence (Rosenblatt)
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The Birthplace of
“Artificial Intelligence”, 1956
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Darmouth workshop, 1956: historical meeting of the precieved founders of AI met:
John McCarthy, Marvin Minsky, Alan Newell, and Herbert Simon.
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A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, 1955. "We
propose that a 2 month, 10 man study of artificial intelligence be carried out during
the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study
is to proceed on the basis of the conjecture that every aspect of learning or any
other feature of intelligence can in principle be so precisely described that a
machine can be made to simulate it." And this marks the debut of the term "artificial
intelligence.“
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50 anniversery of Darmouth workshop
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List of AI-topics
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More AI examples
Common sense reasoning (1980-1990)
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Tweety
Yale Shooting problem
Update vs revise knowledge
The OR gate example: A or B  C
• Observe C=0, vs Do C=0
Chaining theories of actions
Looks-like(P)  is(P)
Make-looks-like(P)  Looks-like(P)
---------------------------------------Makes-looks-like(P) ---is(P) ???
Garage-door example: garage door not included.
• Planning benchmarks
• 8-puzzle, 8-queen, block world, grid-space world
• Cambridge parking example
Smoked fish example… what is this?
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History, continued
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1966-1974 a dose of reality
– Problems with computation
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1969-1979 Knowledge-based systems
– Weak vs. strong methods
– Expert systems:
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Dendral:Inferring molecular structures(Buchanan et. Al. 1969)
Mycin: diagnosing blood infections (Shortliffe et. Al, certainty factors)
Prospector: recommending exploratory drilling (Duda).
– Roger Shank: no syntax only semantics
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1980-1988: AI becomes an industry
– R1: Mcdermott, 1982, order configurations of computer systems
– 1981: Fifth generation
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1986-present: return to neural networks
1987-present :
– AI becomes a science: HMMs, planning, belief network
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1995-present: The emergence of intelligent agents
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Ai agents (SOAR, Newell, Laired, 1987) on the internet, technology in web-based applications ,
recommender systems. Some researchers (Nilsson, McCarthy, Minsky, Winston) express discontent
with the progress of the field. AI should return to human-level AI (they say).
2001-present: The availability of data;
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The knowledge bottleneck may be solved for many applications: learn the information rather than
hand code it .
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State of the art
• Game Playing: Deep Blue defeated the reigning world chess champion
Garry Kasparov in 1997
• Robotics vehicles:
– 2005 Standford robot won DARPA Grand Challenge, driving autonomously 131 miles
along unrehearsed desert trail
– Staneley (Thrun 2006). No hands across America (driving autonomously 98% of the
time from Pittsburgh to San Diego
– 2007 CMU team won DARPA Urban Challenge driving autonomously 55 miles in a city
while adhering to traffic laws and hazards
• Autonomous planning and scheduling:
– During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling
program that involved up to 50,000 vehicles, cargo, and people
– NASA's on-board autonomous planning program controlled the scheduling of
operations for a spacecraft
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Speech recognition
DARPA grand challenge 2003-2005, Robocup
Machine translation (From English to arabic, 2007)
Natural language processing: Watson won Jeopardy (Natural language
processing), IBM 2011. iPhone Siri.
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Robotic links
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Deep Blue: http://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
• Robocup Video
– Soccer Robocupf
• Darpa Challenge
– Darpa’s-challenge-video
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Watson
http://www.youtube.com/watch?v=seNkjYyG3gI
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Today’s class
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What is Artificial Intelligence?
A brief History
Intelligent agents
State of the art
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Agents
• An agent is anything that can be viewed as perceiving its
environment through sensors and acting upon that
environment through actuators
• Human agent: eyes, ears, and other organs for sensors;
hands, legs, mouth, and other body parts for actuators
• Robotic agent: cameras and infrared range finders for
sensors; various motors for actuators
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Agents
• Agents and environments
• Rationality
• PEAS (Performance measure, Environment,
Actuators, Sensors)
• Environment types
• Agent types
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Agents and environments
• The agent function maps from percept histories to actions:
[f: P*  A]
• The agent program runs on the physical architecture to
produce f
• agent = architecture + program
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What’s involved in Intelligence?
• Ability to interact with the real world
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to perceive, understand, and act
e.g., speech recognition and understanding and synthesis
e.g., image understanding
e.g., ability to take actions, have an effect
• Knowledge Representation, Reasoning and Planning
– modeling the external world, given input
– solving new problems, planning and making decisions
– ability to deal with unexpected problems, uncertainties
• Learning and Adaptation
– we are continuously learning and adapting
– our internal models are always being “updated”
• e.g. a baby learning to categorize and recognize animals
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Implementing agents
• Table look-ups
• Autonomy
– All actions are completely specified
– no need in sensing, no autonomy
– example: Monkey and the banana
• Structure of an agent
– agent = architecture + program
– Agent types
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medical diagnosis
Satellite image analysis system
part-picking robot
Interactive English tutor
cooking agent
taxi driver
Graduate student
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Task Environment
• Before we design a rational agent, we must specify
its task environment:
PEAS:
Performance measure
Environment
Actuators
Sensors
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PEAS
• Example: Agent = taxi driver
– Performance measure: Safe, fast, legal, comfortable
trip, maximize profits
– Environment: Roads, other traffic, pedestrians,
customers
– Actuators: Steering wheel, accelerator, brake, signal,
horn
– Sensors: Cameras, sonar, speedometer, GPS, odometer,
engine sensors, keyboard
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PEAS
• Example: Agent = Medical diagnosis system
– Performance measure: Healthy patient, minimize costs,
lawsuits
– Environment: Patient, hospital, staff
– Actuators: Screen display (questions, tests, diagnoses,
treatments, referrals)
– Sensors: Keyboard (entry of symptoms, findings,
patient's answers)
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PEAS
• Example: Agent = Me Part-picking robot
– Performance measure: Percentage of parts in correct
bins
– Environment: Conveyor belt with parts, bins
– Actuators: Jointed arm and hand
– Sensors: Camera, joint angle sensors
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Environment Types
• Fully observable (vs. partially observable): An agent's
sensors give it access to the complete state of the
environment at each point in time.
• Deterministic (vs. stochastic): The next state of the
environment is completely determined by the current state
and the action executed by the agent. (If the environment
is deterministic except for the actions of other agents, then
the environment is strategic)
• Episodic (vs. sequential): An agent’s action is divided into
atomic episodes. Decisions do not depend on previous
decisions/actions.
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Environment Types
• Static (vs. dynamic): The environment is unchanged while
an agent is deliberating. (The environment is semidynamic
if the environment itself does not change with the passage
of time but the agent's performance score does)
• Discrete (vs. continuous): A limited number of distinct,
clearly defined percepts and actions.
How do we represent or abstract or model the world?
• Single agent (vs. multi-agent): An agent operating by itself
in an environment. Does the other agent interfere with my
performance measure?
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Grad student
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current state of decision process
Table Driven Agent.
table lookup
for entire history
Simple reflex agents
NO MEMORY
Fails if environment
is partially observable
example: vacuum cleaner world
Model-based
reflex
agents
description of
current world state
Model the state of the world by:
modeling how the world changes
how it’s actions change the world
•This can work even with partial information
•It’s is unclear what to do
without a clear goal
Goal-based agents
Goals provide reason to prefer one action over the other.
We need to predict the future: we need to plan & search
Utility-based agents
Some solutions to goal states are better than others.
Which one is best is given by a utility function.
Which combination of goals is preferred?
Learning agents
How does an agent improve over time?
By monitoring it’s performance and suggesting
better modeling, new action rules, etc.
Evaluates
current
world
state
changes
action
rules
suggests
explorations
“old agent”=
model world
and decide on
actions
to be taken
Summary
• What is Artificial Intelligence?
– modeling humans thinking, acting, should think, should act.
• History of AI
• Intelligent agents
– We want to build agents that act rationally
• Real-World Applications of AI
– AI is alive and well in various “every day” applications
• many products, systems, have AI components
• Assigned Reading
– Chapters 1 and 2 in the text R&N
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